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Preclinical research in Rett syndrome: setting the foundation for translational success
Authors:David M Katz  Joanne E Berger-Sweeney  James H Eubanks  Monica J Justice  Jeffrey L Neul  Lucas Pozzo-Miller  Mary E Blue  Diana Christian  Jacqueline N Crawley  Maurizio Giustetto  Jacky Guy  C James Howell  Miriam Kron  Sacha B Nelson  Rodney C Samaco  Laura R Schaevitz  Coryse St Hillaire-Clarke  Juan L Young  Huda Y Zoghbi  Laura A Mamounas
Abstract:In September of 2011, the National Institute of Neurological Disorders and Stroke (NINDS), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), the International Rett Syndrome Foundation (IRSF) and the Rett Syndrome Research Trust (RSRT) convened a workshop involving a broad cross-section of basic scientists, clinicians and representatives from the National Institutes of Health (NIH), the US Food and Drug Administration (FDA), the pharmaceutical industry and private foundations to assess the state of the art in animal studies of Rett syndrome (RTT). The aim of the workshop was to identify crucial knowledge gaps and to suggest scientific priorities and best practices for the use of animal models in preclinical evaluation of potential new RTT therapeutics. This review summarizes outcomes from the workshop and extensive follow-up discussions among participants, and includes: (1) a comprehensive summary of the physiological and behavioral phenotypes of RTT mouse models to date, and areas in which further phenotypic analyses are required to enhance the utility of these models for translational studies; (2) discussion of the impact of genetic differences among mouse models, and methodological differences among laboratories, on the expression and analysis, respectively, of phenotypic traits; and (3) definitions of the standards that the community of RTT researchers can implement for rigorous preclinical study design and transparent reporting to ensure that decisions to initiate costly clinical trials are grounded in reliable preclinical data.
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